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Machine Learning is a science that enables machines (especially computers) to learn from environments and make own decisions.
Research Activities
- The Machine Learning Laboratory (MLL) carries out research and develops different theoretical foundations for machine learning such as:
- Reinforcement Learning, Deep Learning, Statistical Learning Theory, Multi-agent Systems, Game Theory and Mechanism Design, Blockchains, Explainable and Fair AI
- How machines and multi-agent systems should help in planning activities by learning from environments
- How learning gets affected if different machine learning and multi-agent systems algorithms are trying compete instead of cooperating
- How machines should learn in the presence of noisy environment or partial supervision
- Role of deep learning in planning, reinforcement learning, AI in game theory, blockchains and its applications
- For more details: Research Projects
Recent News
- One of our papers titled “Differentially Private Federated Combinatorial Bandits with Constraints” got accepted in ECML-PKDD 2022
- Our paper titled “An Autonomous Intelligent Broker for Smart-grids” got accepted in IJCAI, 2022
- Our paper on “Individual Fairness in Feature-Based Pricing for Monopoly Markets” got accepted in UAI 2022
- One of our papers titled “Active Learning with BandIt Feedback” got accepted in PAKDD 2022
- One of our papers on Layered Blockchain protocols got accepted in IEEE ICBC 2022.
- Three of our papers get accepted in AAMAS 2022
- Our paper, ‘How Private Is Your RL Policy? An Inverse RL Based Analysis Framework’ got accepted for presentation at AAAI 2022
- MLL’s Team in collaboration with TCS, VidyutVanika wins the PowerTAC 2021 Tournament
- One of our paper got accepted for presentation at Young Researcher’s Symposium, CODS-COMAD 2022